code: def _apply_mask(
      self, F, score: Tensor, key_mask: Optional[Tensor]
  ) -> Tensor:
      if not self.bidirectional:
          k_idx = F.contrib.arange_like(score, axis=-1)
          k_idx = (
              k_idx.expand_dims(axis=0)
              .expand_dims(axis=0)
              .expand_dims(axis=0)
          )
          q_idx = F.contrib.arange_like(score, axis=-2)
          q_idx = (
              q_idx.expand_dims(axis=-1)
              .expand_dims(axis=0)
              .expand_dims(axis=0)
          )
          unidir_mask = F.broadcast_lesser_equal(k_idx, q_idx)
          unidir_mask = F.broadcast_like(unidir_mask, score)
          score = F.where(unidir_mask, score, F.ones_like(score) * 1e-9)
      if key_mask is not None:
          mem_mask = key_mask.squeeze(axis=-1)
          mem_mask = mem_mask.expand_dims(axis=1)  # head
          mem_mask = mem_mask.expand_dims(axis=2)  # query
          mem_mask = F.broadcast_like(mem_mask, score)
          score = F.where(mem_mask, score, F.ones_like(score) * 1e-9)
      return score
  
imports: import math
  from typing import List, Optional, Tuple
  from mxnet import init
  from mxnet.gluon import HybridBlock, Parameter, nn
  from mxnet.gluon.contrib.nn import HybridConcurrent
  from gluonts.core.component import validated
  from gluonts.mx import Tensor
label_desc: Use image node_id 5 for calling the SelfAttention obj's underlying member method code with example usage: obj._apply_mask(F, score, key_mask) and returns: score